Brian Barr

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 21

Top Topics

Machine Learning

Top Venues

IEEE Trans. Vis. Comput. Graph.

Publications

Parikshit Solunke, Vitoria Guardieiro, João Rulff, Peter Xenopoulos, Gromit Yeuk-Yin Chan, Brian Barr, Luis Gustavo Nonato, Cláudio T. Silva
MOUNTAINEER: Topology-Driven Visual Analytics for Comparing Local Explanations. CoRR (2024)
Wei Zhang, Brian Barr, John Paisley
Gaussian Process Neural Additive Models. CoRR (2024)
Jun Yuan, Brian Barr, Kyle Overton, Enrico Bertini
Visual Exploration of Machine Learning Model Behavior With Hierarchical Surrogate Rule Sets. IEEE Trans. Vis. Comput. Graph. 30 (2) (2024)
Wei Zhang, Brian Barr, John Paisley
Gaussian Process Neural Additive Models. AAAI (2024)
Evandro S. Ortigossa, Fábio F. Dias, Brian Barr, Cláudio T. Silva, Luis Gustavo Nonato
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients. CoRR (2024)
Peter Xenopoulos, João Rulff, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva
Calibrate: Interactive Analysis of Probabilistic Model Output. IEEE Trans. Vis. Comput. Graph. 29 (1) (2023)
Brian Barr, Noah Fatsi, Leif Hancox-Li, Peter Richter, Daniel Proano, Caleb Mok
The Disagreement Problem in Faithfulness Metrics. CoRR (2023)
Jun Yuan, Gromit Yeuk-Yin Chan, Brian Barr, Kyle Overton, Kim Rees, Luis Gustavo Nonato, Enrico Bertini, Cláudio T. Silva
SUBPLEX: A Visual Analytics Approach to Understand Local Model Explanations at the Subpopulation Level. IEEE Computer Graphics and Applications 42 (6) (2022)
Peter Xenopoulos, Gromit Chan, Harish Doraiswamy, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva
GALE: Globally Assessing Local Explanations. TAG-ML (2022)
Wei Zhang, Brian Barr, John Paisley
Understanding Counterfactual Generation using Maximum Mean Discrepancy. ICAIF (2022)
Wei Zhang, Brian Barr, John Paisley
An Interpretable Deep Classifier for Counterfactual Generation. ICAIF (2022)
Isha Hameed, Samuel Sharpe, Daniel Barcklow, Justin Au-Yeung, Sahil Verma, Jocelyn Huang, Brian Barr, C. Bayan Bruss
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence. CoRR (2022)
Peter Xenopoulos, Gromit Yeuk-Yin Chan, Harish Doraiswamy, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva
Topological Representations of Local Explanations. CoRR (2022)
Peter Xenopoulos, João Rulff, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva
Calibrate: Interactive Analysis of Probabilistic Model Output. CoRR (2022)
Jun Yuan, Brian Barr, Kyle Overton, Enrico Bertini
Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets. CoRR (2022)
Rathakrishnan Bhaskaran, Ramakrishnan Kannan, Brian Barr, Stephan Priebe
Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation. IEEE BigData (2021)
Brian Barr, Matthew R. Harrington, Samuel Sharpe, C. Bayan Bruss
Counterfactual Explanations via Latent Space Projection and Interpolation. CoRR (2021)
Rachana Balasubramanian, Samuel Sharpe, Brian Barr, Jason D. Wittenbach, C. Bayan Bruss
Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations. CoRR (2020)
Gromit Yeuk-Yin Chan, Enrico Bertini, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva
Melody: Generating and Visualizing Machine Learning Model Summary to Understand Data and Classifiers Together. CoRR (2020)
Gromit Yeuk-Yin Chan, Jun Yuan, Kyle Overton, Brian Barr, Kim Rees, Luis Gustavo Nonato, Enrico Bertini, Cláudio T. Silva
SUBPLEX: Towards a Better Understanding of Black Box Model Explanations at the Subpopulation Level. CoRR (2020)
Brian Barr, Ke Xu, Cláudio T. Silva, Enrico Bertini, Robert Reilly, C. Bayan Bruss, Jason D. Wittenbach
Towards Ground Truth Explainability on Tabular Data. CoRR (2020)